r = getOption("repos")
r["CRAN"] = "http://cran.us.r-project.org"
options(repos = r)
install.packages("readxl")
install.packages("corrplot")
## package 'corrplot' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\anna\AppData\Local\Temp\RtmpwlWGY8\downloaded_packages
install.packages("plotly")
## package 'plotly' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\anna\AppData\Local\Temp\RtmpwlWGY8\downloaded_packages
library("readxl")
library(dplyr)
library(tidyr)
library(ggplot2)
library(mlbench)
library(corrplot)
library(caret)
library(plotly)
Path to directory with data
dataDirectoryPath <- 'C:/Users/anna/OneDrive/Nauka/II semestr II stopień/Zaawansowana Eksploracja Danych/Data pack/Data pack/'
Loading CurrencyExchangeRates.csv file
currencyExchangeRatesPath <- paste0(dataDirectoryPath, 'CurrencyExchangeRates.csv')
currencyExchangeRates <- read.csv(currencyExchangeRatesPath)
Loading Gold prices.csv file
goldPricesPath <- paste0(dataDirectoryPath, 'Gold prices.csv')
goldPrices <- read.csv(goldPricesPath)
Loading S&P Composite.csv file
spCompositePath <- paste0(dataDirectoryPath, 'S&P Composite.csv')
spComposite <- read.csv(spCompositePath)
Loading World_Development_Indicators.xlsx file
worldDevelopmentIndicatorsPath <- paste0(dataDirectoryPath, 'World_Development_Indicators.xlsx')
worldDevelopmentIndicators <- read_excel(worldDevelopmentIndicatorsPath)
cleanedWorldDevelopmentIndicators <- worldDevelopmentIndicators[c(1:42600, 43666:43878),]
cleanedWorldDevelopmentIndicators <- cleanedWorldDevelopmentIndicators %>%
mutate_at(vars(!c('Country Name', 'Country Code', 'Series Name', 'Series Code')), as.numeric) %>%
pivot_longer(!c('Country Name', 'Country Code', 'Series Name', 'Series Code'), names_to = "year", values_to="value")
cleanedWorldDevelopmentIndicators$year <- substr(cleanedWorldDevelopmentIndicators$year,1,4)
cleanedWorldDevelopmentIndicatorsResult <- cleanedWorldDevelopmentIndicators %>%
pivot_wider(names_from = c('Series Name', 'Series Code'), values_from = 'value', names_glue = "{`Series Name`}") %>%
mutate_at(vars(year), as.numeric)
cleanedWorldDevelopmentIndicatorsResult[cleanedWorldDevelopmentIndicatorsResult == ".." | cleanedWorldDevelopmentIndicatorsResult == ""] <- NA
cleanedWorldDevelopmentIndicatorsResult <- cleanedWorldDevelopmentIndicatorsResult %>% select_if(colSums(!is.na(.)) > (nrow(cleanedWorldDevelopmentIndicatorsResult)/2))
n <- ncol(cleanedWorldDevelopmentIndicatorsResult)
missingValuesByCountry <- cleanedWorldDevelopmentIndicatorsResult %>%
group_by(`Country Name`) %>%
summarise_all(~sum(is.na(.))) %>%
transmute(`Country Name`, sumNA = rowSums(.[-1])) %>%
arrange(desc(sumNA))
knitr::kable(head(missingValuesByCountry, 20))
| Country Name | sumNA |
|---|---|
| Isle of Man | 4544 |
| Sint Maarten (Dutch part) | 4487 |
| Monaco | 4432 |
| American Samoa | 4389 |
| San Marino | 4378 |
| British Virgin Islands | 4318 |
| Turks and Caicos Islands | 4207 |
| Channel Islands | 4164 |
| Cayman Islands | 4127 |
| Kosovo | 4113 |
| Liechtenstein | 4026 |
| Guam | 3826 |
| Gibraltar | 3809 |
| Andorra | 3789 |
| Virgin Islands (U.S.) | 3781 |
| Faroe Islands | 3656 |
| South Sudan | 3576 |
| Tuvalu | 3568 |
| Curacao | 3556 |
| Greenland | 3556 |
worldIndicatorsComplementedDf <- cleanedWorldDevelopmentIndicatorsResult %>%
group_by(`Country Name`) %>%
mutate_each(funs(replace(., which(is.na(.)), min(., na.rm=TRUE)))) %>%
mutate_each(funs(replace(., which(is.infinite(.)), 0)))
names(worldIndicatorsComplementedDf) <- gsub(" ", "_", names(worldIndicatorsComplementedDf))
names(cleanedWorldDevelopmentIndicatorsResult) <- gsub(" ", "_", names(cleanedWorldDevelopmentIndicatorsResult))
knitr::kable(head(worldIndicatorsComplementedDf))
| Country_Name | Country_Code | year | Urban_population_growth_(annual_%) | Urban_population_(%_of_total_population) | Urban_population | Transport_services_(%_of_commercial_service_exports) | Transport_services_(%_of_commercial_service_imports) | Trade_in_services_(%_of_GDP) | Trade_(%_of_GDP) | Total_natural_resources_rents_(%_of_GDP) | Total_greenhouse_gas_emissions_(kt_of_CO2_equivalent) | Taxes_less_subsidies_on_products_(current_US\()| Taxes_less_subsidies_on_products_(current_LCU)| Taxes_less_subsidies_on_products_(constant_LCU)| Survival_to_age_65,_female_(%_of_cohort)| Survival_to_age_65,_male_(%_of_cohort)| Services,_value_added_(%_of_GDP)| Service_imports_(BoP,_current_US\)) | Service_exports_(BoP,_current_US\()| Secondary_education,_pupils| Rural_population_growth_(annual_%)| Rural_population_(%_of_total_population)| Rural_population| Renewable_energy_consumption_(%_of_total_final_energy_consumption)| Renewable_electricity_output_(%_of_total_electricity_output)| Pupil-teacher_ratio,_primary| Primary_income_payments_(BoP,_current_US\)) | Primary_income_receipts_(BoP,_current_US\()| Primary_school_starting_age_(years)| Portfolio_investment,_net_(BoP,_current_US\)) | Portfolio_equity,net_inflows(BoP,_current_US\()| Population,_total| Population,_male| Population,_male_(%_of_total_population)| Population,_female_(%_of_total_population)| Population,_female| Population_in_urban_agglomerations_of_more_than_1_million| Population_in_the_largest_city_(%_of_urban_population)| Population_in_largest_city| Population_growth_(annual_%)| Population_density_(people_per_sq._km_of_land_area)| Population_ages_65_and_above_(%_of_total_population)| Population_ages_15-64_(%_of_total_population)| Population_ages_0-14_(%_of_total_population)| Number_of_under-five_deaths| Nitrous_oxide_emissions_(thousand_metric_tons_of_CO2_equivalent)| Nitrous_oxide_emissions_in_energy_sector_(%_of_total)| Net_primary_income_(Net_income_from_abroad)_(current_US\)) | Net_primary_income_(Net_income_from_abroad)_(current_LCU) | Net_primary_income_(BoP,_current_US\()| Net_official_development_assistance_received_(current_US\)) | Net_domestic_credit_(current_LCU) | Natural_gas_rents_(%_of_GDP) | Mortality_rate,infant(per_1,000_live_births) | Methane_emissions_(kt_of_CO2_equivalent) | Methane_emissions_in_energy_sector_(thousand_metric_tons_of_CO2_equivalent) | Merchandise_exports_to_high-income_economies_(%_of_total_merchandise_exports) | Manufacturing,value_added(%_of_GDP) | Life_expectancy_at_birth,total(years) | Land_area_(sq._km) | Labor_force,_total | Inflation,consumer_prices(annual_%) | Individuals_using_the_Internet_(%_of_population) | Imports_of_goods_and_services_(current_US\()| Imports_of_goods_and_services_(%_of_GDP)| Gross_savings_(%_of_GDP)| Gross_national_expenditure_(%_of_GDP)| Gross_national_expenditure_(current_US\)) | Gross_savings_(current_US\()| Gross_domestic_savings_(%_of_GDP)| Gross_domestic_savings_(current_US\)) | Goods_exports_(BoP,_current_US\()| Goods_imports_(BoP,_current_US\)) | GDP_per_capita_(current_US\()| GDP_per_capita_growth_(annual_%)| GDP_growth_(annual_%)| GDP_(current_US\)) | Fuel_exports_(%_of_merchandise_exports) | Fuel_imports_(%_of_merchandise_imports) | Food_exports_(%_of_merchandise_exports) | Food_imports_(%_of_merchandise_imports) | Exports_of_goods_and_services_(current_US\()| Exports_of_goods_and_services_(annual_%_growth)| Electricity_production_from_renewable_sources,_excluding_hydroelectric_(kWh)| Electricity_production_from_renewable_sources,_excluding_hydroelectric_(%_of_total)| Electricity_production_from_oil,_gas_and_coal_sources_(%_of_total)| Electricity_production_from_coal_sources_(%_of_total)| Electricity_production_from_hydroelectric_sources_(%_of_total)| Electricity_production_from_natural_gas_sources_(%_of_total)| Electricity_production_from_nuclear_sources_(%_of_total)| Consumer_price_index_(2010_=_100)| CO2_emissions_from_solid_fuel_consumption_(%_of_total)| CO2_emissions_from_solid_fuel_consumption_(kt)| CO2_emissions_from_transport_(%_of_total_fuel_combustion)| CO2_intensity_(kg_per_kg_of_oil_equivalent_energy_use)| CO2_emissions_from_residential_buildings_and_commercial_and_public_services_(%_of_total_fuel_combustion)| CO2_emissions_from_other_sectors,_excluding_residential_buildings_and_commercial_and_public_services_(%_of_total_fuel_combustion)| CO2_emissions_from_manufacturing_industries_and_construction_(%_of_total_fuel_combustion)| CO2_emissions_from_liquid_fuel_consumption_(kt)| CO2_emissions_from_liquid_fuel_consumption_(%_of_total)| CO2_emissions_from_gaseous_fuel_consumption_(kt)| CO2_emissions_from_gaseous_fuel_consumption_(%_of_total)| CO2_emissions_from_electricity_and_heat_production,_total_(%_of_total_fuel_combustion)| CO2_emissions_(metric_tons_per_capita)| CO2_emissions_(kt)| CO2_emissions_(kg_per_2010_US\)_of_GDP) | Birth_rate,crude(per_1,000_people) | Access_to_electricity_(%_of_population) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Afghanistan | AFG | 1970 | 5.748488 | 11.643 | 1300949 | 6.982691 | 59.19951 | 4.926239 | 21.72811 | 0.3028816 | 14306.62 | 60000000 | 2700000000 | 7606457000 | 27.72562 | 23.52467 | 36.15115 | 103400000 | 8200000 | 116174 | 2.121114 | 88.357 | 9872705 | 11.5591 | 67.7305 | 41.22332 | 6900000 | 19200000 | 7 | -29571652 | 0 | 11173654 | 5697024 | 50.98622 | 49.01378 | 5476630 | 471891 | 36.27283 | 471891 | 2.536744 | 17.11493 | 2.631613 | 53.04314 | 44.32524 | 164463 | 3042.256 | 4.823610 | 35557778 | 1600100000 | -35470046 | 27610001 | 10711736612 | 0.0000000 | 200.9 | 10202.00 | 1166.628 | 33.71281 | 3.530422 | 37.409 | 652860 | 3066275 | -6.811161 | 0 | 208888900 | 11.94411 | 0 | 102.1601 | 1786664529 | 0 | 3.303681 | 57777629 | 252300000 | 623500000 | 156.5188 | -4.168191 | -1.934778 | 1748886596 | 16.94615 | 6.105695 | 36.06465 | 20.54160 | 171111104 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 63.52339 | 26.09649 | 436.373 | 0 | 0 | 0 | 0 | 0 | 671.061 | 40.13158 | 216.353 | 12.93860 | 0 | 0.1496513 | 1672.152 | 0.1371468 | 51.502 | 22.29527 |
| Afghanistan | AFG | 1971 | 5.860102 | 12.021 | 1379464 | 6.982691 | 59.19951 | 4.926239 | 27.06314 | 0.3739563 | 14391.78 | 64444444 | 2900000000 | 7606457000 | 28.39374 | 24.14596 | 36.15115 | 103400000 | 8200000 | 134069 | 2.236404 | 87.979 | 10095986 | 11.5591 | 67.7305 | 39.10539 | 6900000 | 19200000 | 7 | -29571652 | 0 | 11475450 | 5845351 | 50.93788 | 49.06212 | 5630099 | 495303 | 35.90547 | 495303 | 2.665128 | 17.57720 | 2.635235 | 52.69327 | 44.67150 | 165306 | 3006.138 | 4.863614 | 37777778 | 1700000000 | -35470046 | 44439999 | 11670336612 | 0.0000000 | 197.1 | 10201.50 | 1178.829 | 42.71271 | 3.530422 | 37.930 | 652860 | 3066275 | -6.811161 | 0 | 295555549 | 16.14080 | 0 | 105.2184 | 1926664351 | 0 | 0.242728 | 4444613 | 252300000 | 623500000 | 159.5675 | -4.168191 | -1.934778 | 1831108971 | 14.55330 | 5.000146 | 27.02128 | 25.43237 | 199999989 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 63.52339 | 18.95551 | 359.366 | 0 | 0 | 0 | 0 | 0 | 748.068 | 39.45841 | 440.040 | 23.21083 | 0 | 0.1652082 | 1895.839 | 0.1371468 | 51.411 | 22.29527 |
| Afghanistan | AFG | 1972 | 5.899299 | 12.410 | 1463291 | 6.982691 | 59.19951 | 4.926239 | 32.86908 | 0.4352624 | 13040.85 | 60000000 | 2700000000 | 7606457000 | 29.06186 | 24.76724 | 36.15115 | 103400000 | 8200000 | 153060 | 2.271405 | 87.590 | 10327931 | 11.5591 | 67.7305 | 40.38314 | 6900000 | 19200000 | 7 | -29571652 | 0 | 11791222 | 6000895 | 50.89290 | 49.10710 | 5790327 | 528508 | 36.11776 | 528508 | 2.714539 | 18.06087 | 2.627456 | 52.43530 | 44.93724 | 166120 | 2530.158 | 5.652229 | 35553333 | 1599900000 | -35470046 | 55180000 | 13556437512 | 0.0041971 | 193.4 | 9170.59 | 1262.196 | 35.24140 | 3.530422 | 38.461 | 652860 | 3066275 | -6.811161 | 0 | 288888878 | 18.10585 | 0 | 103.3426 | 1648888947 | 0 | 3.203335 | 51110980 | 252300000 | 623500000 | 135.3172 | -4.168191 | -1.934778 | 1595555476 | 13.73092 | 6.813822 | 37.23807 | 28.46417 | 235555544 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 63.52339 | 12.44019 | 190.684 | 0 | 0 | 0 | 0 | 0 | 627.057 | 40.90909 | 300.694 | 19.61722 | 0 | 0.1299955 | 1532.806 | 0.1371468 | 51.303 | 22.29527 |
| Afghanistan | AFG | 1973 | 5.823573 | 12.809 | 1551037 | 6.982691 | 59.19951 | 4.926239 | 27.69231 | 0.8714832 | 13535.75 | 64444444 | 2900000000 | 7606457000 | 29.83817 | 25.49372 | 36.15115 | 103400000 | 8200000 | 165346 | 2.202488 | 87.191 | 10557926 | 11.5591 | 67.7305 | 38.32161 | 6900000 | 19200000 | 7 | -29571652 | 0 | 12108963 | 6157843 | 50.85359 | 49.14641 | 5951120 | 573161 | 36.95341 | 573161 | 2.659057 | 18.54756 | 2.609505 | 52.25243 | 45.13807 | 166562 | 2674.404 | 5.255055 | 37775556 | 1699900000 | -35470046 | 55720001 | 13941837512 | 0.0479294 | 189.4 | 9403.54 | 1173.216 | 29.77436 | 3.530422 | 39.003 | 652860 | 3066275 | -6.811161 | 0 | 255555562 | 14.74359 | 0 | 101.7949 | 1764444431 | 0 | 5.512817 | 95555493 | 252300000 | 623500000 | 143.1446 | -4.168191 | -1.934778 | 1733333264 | 11.61954 | 6.565215 | 48.62099 | 23.86529 | 224444438 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 63.52339 | 19.01566 | 311.695 | 0 | 0 | 0 | 0 | 0 | 704.064 | 42.95302 | 333.697 | 20.35794 | 0 | 0.1353666 | 1639.149 | 0.1371468 | 51.184 | 22.29527 |
| Afghanistan | AFG | 1974 | 5.630224 | 13.219 | 1640869 | 6.982691 | 59.19951 | 4.926239 | 28.86598 | 1.1527195 | 14945.97 | 120000000 | 5400000000 | 7606457000 | 30.61448 | 26.22020 | 36.15115 | 103400000 | 8200000 | 172797 | 2.008176 | 86.781 | 10772091 | 11.5591 | 67.7305 | 37.21537 | 6900000 | 19200000 | 7 | -29571652 | 0 | 12412960 | 6308583 | 50.82255 | 49.17745 | 6104377 | 621656 | 37.88578 | 621656 | 2.479517 | 19.01320 | 2.583512 | 52.13682 | 45.27967 | 166690 | 2937.318 | 5.028316 | 46666667 | 2100000000 | -35470046 | 48910000 | 15681237512 | 0.1701138 | 185.5 | 9987.93 | 1304.340 | 24.15658 | 3.530422 | 39.558 | 652860 | 3066275 | -6.811161 | 0 | 320000000 | 14.84536 | 0 | 100.8247 | 2173333344 | 0 | 7.938141 | 171111036 | 252300000 | 623500000 | 173.6536 | -4.168191 | -1.934778 | 2155555498 | 13.97424 | 9.325608 | 43.91324 | 22.88171 | 302222222 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 63.52339 | 15.86998 | 304.361 | 0 | 0 | 0 | 0 | 0 | 770.070 | 40.15296 | 399.703 | 20.84130 | 0 | 0.1545031 | 1917.841 | 0.1371468 | 51.058 | 22.29527 |
| Afghanistan | AFG | 1975 | 5.343228 | 13.641 | 1730929 | 6.982691 | 59.19951 | 4.926239 | 26.94836 | 1.6405264 | 14574.16 | 91111111 | 4100000000 | 7606457000 | 31.39079 | 26.94668 | 36.15115 | 103400000 | 8200000 | 185723 | 1.713261 | 86.359 | 10958235 | 11.5591 | 67.7305 | 37.30693 | 6900000 | 19200000 | 7 | -29571652 | 0 | 12689164 | 6446273 | 50.80140 | 49.19860 | 6242891 | 674254 | 38.95330 | 674254 | 2.200731 | 19.43627 | 2.551445 | 52.09805 | 45.35050 | 166442 | 3153.723 | 4.835251 | 51111111 | 2300000000 | -35470046 | 66980003 | 15551937512 | 0.4362939 | 181.5 | 10476.60 | 1298.326 | 29.40127 | 3.530422 | 40.128 | 652860 | 3066275 | -6.811161 | 0 | 337777778 | 14.27230 | 0 | 101.5962 | 2404444524 | 0 | 8.169009 | 193333202 | 252300000 | 623500000 | 186.5108 | -4.168191 | -1.934778 | 2366666616 | 20.29962 | 7.743069 | 36.42858 | 24.42357 | 300000007 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 63.52339 | 18.79310 | 399.703 | 0 | 0 | 0 | 0 | 0 | 876.413 | 41.20690 | 476.710 | 22.41379 | 0 | 0.1676123 | 2126.860 | 0.1371468 | 50.930 | 22.29527 |
goldPricesYearly <- goldPrices
goldPricesYearly$year <- substr(goldPrices$Date,1,4)
goldPricesYearly <- group_by(goldPricesYearly, year)
goldPricesYearly <- summarize(goldPricesYearly,
gold_usd = mean(`USD..AM.`, na.rm=TRUE),
gold_gbp = mean(`GBP..AM.`, na.rm=TRUE),
gold_euro = mean(`EURO..AM.`, na.rm=TRUE))
knitr::kable(head(goldPricesYearly))
| year | gold_usd | gold_gbp | gold_euro |
|---|---|---|---|
| 1968 | 38.82947 | 16.21790 | NaN |
| 1969 | 41.09988 | 17.19545 | NaN |
| 1970 | 35.96369 | 15.01211 | NaN |
| 1971 | 40.80680 | 16.67321 | NaN |
| 1972 | 58.17378 | 23.37925 | NaN |
| 1973 | 97.11735 | 39.54271 | NaN |
spCompositeYearly <- spComposite
spCompositeYearly$year <- substr(spComposite$Year,1,4)
spCompositeYearly <- group_by(spCompositeYearly, year)
spCompositeYearly <- summarize(spCompositeYearly,
spComposite = mean(`S.P.Composite`, na.rm=TRUE),
dividend = mean(Dividend, na.rm=TRUE),
earnings = mean(Earnings, na.rm=TRUE),
cpi = mean(CPI, na.rm=TRUE),
longInterestRate = mean(`Long.Interest.Rate`, na.rm=TRUE),
realPrice = mean(`Real.Price`, na.rm=TRUE),
realDividend = mean(`Real.Dividend`, na.rm=TRUE),
realEarnings = mean(`Real.Earnings`, na.rm=TRUE),
cyclicallyAdjustedPERattio = mean(`Cyclically.Adjusted.PE.Ratio`,na.rm=TRUE),
)
knitr::kable(head(spCompositeYearly))
| year | spComposite | dividend | earnings | cpi | longInterestRate | realPrice | realDividend | realEarnings | cyclicallyAdjustedPERattio |
|---|---|---|---|---|---|---|---|---|---|
| 1871 | 4.691667 | 0.2600000 | 0.4000000 | 12.40064 | 5.338333 | 103.3978 | 5.727707 | 8.811857 | NaN |
| 1872 | 5.029167 | 0.2816667 | 0.4162500 | 12.92396 | 5.460833 | 106.2341 | 5.949608 | 8.793324 | NaN |
| 1873 | 4.801667 | 0.3162500 | 0.4462500 | 12.67816 | 5.529583 | 103.2745 | 6.823079 | 9.625514 | NaN |
| 1874 | 4.570000 | 0.3300000 | 0.4600000 | 11.94077 | 5.286667 | 104.5140 | 7.549957 | 10.524182 | NaN |
| 1875 | 4.447500 | 0.3137500 | 0.4058333 | 11.26684 | 4.850000 | 107.7608 | 7.601466 | 9.825189 | NaN |
| 1876 | 4.060833 | 0.3000000 | 0.3166667 | 10.50566 | 4.525833 | 105.5173 | 7.801827 | 8.229060 | NaN |
WorldDevelopmentIndicators summary
knitr::kable(summary(cleanedWorldDevelopmentIndicatorsResult))
| Country_Name | Country_Code | year | Urban_population_growth_(annual_%) | Urban_population_(%_of_total_population) | Urban_population | Transport_services_(%_of_commercial_service_exports) | Transport_services_(%_of_commercial_service_imports) | Trade_in_services_(%_of_GDP) | Trade_(%_of_GDP) | Total_natural_resources_rents_(%_of_GDP) | Total_greenhouse_gas_emissions_(kt_of_CO2_equivalent) | Taxes_less_subsidies_on_products_(current_US\() |Taxes_less_subsidies_on_products_(current_LCU) |Taxes_less_subsidies_on_products_(constant_LCU) |Survival_to_age_65,_female_(%_of_cohort) |Survival_to_age_65,_male_(%_of_cohort) |Services,_value_added_(%_of_GDP) |Service_imports_(BoP,_current_US\)) | Service_exports_(BoP,_current_US\() |Secondary_education,_pupils |Rural_population_growth_(annual_%) |Rural_population_(%_of_total_population) |Rural_population |Renewable_energy_consumption_(%_of_total_final_energy_consumption) |Renewable_electricity_output_(%_of_total_electricity_output) |Pupil-teacher_ratio,_primary |Primary_income_payments_(BoP,_current_US\)) | Primary_income_receipts_(BoP,_current_US\() |Primary_school_starting_age_(years) |Portfolio_investment,_net_(BoP,_current_US\)) | Portfolio_equity,net_inflows(BoP,_current_US\() |Population,_total |Population,_male |Population,_male_(%_of_total_population) |Population,_female_(%_of_total_population) |Population,_female |Population_in_urban_agglomerations_of_more_than_1_million |Population_in_the_largest_city_(%_of_urban_population) |Population_in_largest_city |Population_growth_(annual_%) |Population_density_(people_per_sq._km_of_land_area) |Population_ages_65_and_above_(%_of_total_population) |Population_ages_15-64_(%_of_total_population) |Population_ages_0-14_(%_of_total_population) |Number_of_under-five_deaths |Nitrous_oxide_emissions_(thousand_metric_tons_of_CO2_equivalent) |Nitrous_oxide_emissions_in_energy_sector_(%_of_total) |Net_primary_income_(Net_income_from_abroad)_(current_US\)) | Net_primary_income_(Net_income_from_abroad)_(current_LCU) | Net_primary_income_(BoP,_current_US\() |Net_official_development_assistance_received_(current_US\)) | Net_domestic_credit_(current_LCU) | Natural_gas_rents_(%_of_GDP) | Mortality_rate,infant(per_1,000_live_births) | Methane_emissions_(kt_of_CO2_equivalent) | Methane_emissions_in_energy_sector_(thousand_metric_tons_of_CO2_equivalent) | Merchandise_exports_to_high-income_economies_(%_of_total_merchandise_exports) | Manufacturing,value_added(%_of_GDP) | Life_expectancy_at_birth,total(years) | Land_area_(sq._km) | Labor_force,_total | Inflation,consumer_prices(annual_%) | Individuals_using_the_Internet_(%_of_population) | Imports_of_goods_and_services_(current_US\() |Imports_of_goods_and_services_(%_of_GDP) |Gross_savings_(%_of_GDP) |Gross_national_expenditure_(%_of_GDP) |Gross_national_expenditure_(current_US\)) | Gross_savings_(current_US\() |Gross_domestic_savings_(%_of_GDP) |Gross_domestic_savings_(current_US\)) | Goods_exports_(BoP,_current_US\() |Goods_imports_(BoP,_current_US\)) | GDP_per_capita_(current_US\() |GDP_per_capita_growth_(annual_%) |GDP_growth_(annual_%) |GDP_(current_US\)) | Fuel_exports_(%_of_merchandise_exports) | Fuel_imports_(%_of_merchandise_imports) | Food_exports_(%_of_merchandise_exports) | Food_imports_(%_of_merchandise_imports) | Exports_of_goods_and_services_(current_US\() |Exports_of_goods_and_services_(annual_%_growth) |Electricity_production_from_renewable_sources,_excluding_hydroelectric_(kWh) |Electricity_production_from_renewable_sources,_excluding_hydroelectric_(%_of_total) |Electricity_production_from_oil,_gas_and_coal_sources_(%_of_total) |Electricity_production_from_coal_sources_(%_of_total) |Electricity_production_from_hydroelectric_sources_(%_of_total) |Electricity_production_from_natural_gas_sources_(%_of_total) |Electricity_production_from_nuclear_sources_(%_of_total) |Consumer_price_index_(2010_=_100) |CO2_emissions_from_solid_fuel_consumption_(%_of_total) |CO2_emissions_from_solid_fuel_consumption_(kt) |CO2_emissions_from_transport_(%_of_total_fuel_combustion) |CO2_intensity_(kg_per_kg_of_oil_equivalent_energy_use) |CO2_emissions_from_residential_buildings_and_commercial_and_public_services_(%_of_total_fuel_combustion) |CO2_emissions_from_other_sectors,_excluding_residential_buildings_and_commercial_and_public_services_(%_of_total_fuel_combustion) |CO2_emissions_from_manufacturing_industries_and_construction_(%_of_total_fuel_combustion) |CO2_emissions_from_liquid_fuel_consumption_(kt) |CO2_emissions_from_liquid_fuel_consumption_(%_of_total) |CO2_emissions_from_gaseous_fuel_consumption_(kt) |CO2_emissions_from_gaseous_fuel_consumption_(%_of_total) |CO2_emissions_from_electricity_and_heat_production,_total_(%_of_total_fuel_combustion) |CO2_emissions_(metric_tons_per_capita) |CO2_emissions_(kt) |CO2_emissions_(kg_per_2010_US\)_of_GDP) | Birth_rate,crude(per_1,000_people) | Access_to_electricity_(%_of_population) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Length:10251 | Length:10251 | Min. :1970 | Min. :-187.142 | Min. : 2.845 | Min. : 1267 | Min. :-381.37 | Min. : 0.292 | Min. : 1.165 | Min. : 0.021 | Min. : 0.0000 | Min. : 1 | Min. :-14435512683 | Min. :-125496297000000 | Min. :-98323116751800 | Min. : 6.464 | Min. : 1.477 | Min. :10.86 | Min. : 912800 | Min. : 0 | Min. : 0 | Min. :-235.7924 | Min. : 0.00 | Min. : 0 | Min. : 0.000 | Min. : 0.00 | Min. : 5.226 | Min. : -218731249 | Min. : -50607238 | Min. :4.000 | Min. :-807954000000 | Min. :-244072000000 | Min. : 5740 | Min. : 25278 | Min. :44.37 | Min. :23.29 | Min. : 25864 | Min. : 34329 | Min. : 2.867 | Min. : 18587 | Min. :-10.9551 | Min. : 0.136 | Min. : 0.6856 | Min. :45.45 | Min. :11.05 | Min. : 0 | Min. : 0.0 | Min. : 0.000 | Min. :-99049385578 | Min. :-481321056610000 | Min. :-105172643986 | Min. : -989940002 | Min. : -54237641050700 | Min. : 0.0000 | Min. : 1.50 | Min. : 0 | Min. : 0 | Min. : 0.0074 | Min. : 0.000 | Min. :18.91 | Min. : 2 | Min. : 31205 | Min. : -18.109 | Min. : 0.000 | Min. : 0 | Min. : 0.00 | Min. :-236.27 | Min. : 21.21 | Min. : 18046707 | Min. : -26010016894 | Min. :-141.97 | Min. : -7621878298 | Min. : 199074 | Min. : 5153121 | Min. : 22.8 | Min. :-64.9924 | Min. :-64.047 | Min. : 8824448 | Min. : 0.000 | Min. : 0.009 | Min. : 0.000 | Min. : 0.474 | Min. : 693281 | Min. : -96.4 | Min. : 0 | Min. : 0.000 | Min. : 0.00 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.00 | Min. : -4.324 | Min. : -114 | Min. : 0.00 | Min. : 0.054 | Min. : 0.000 | Min. :-2.326 | Min. : 0.00 | Min. : -161 | Min. : -6.089 | Min. : -147 | Min. : -0.7295 | Min. : 0.00 | Min. : 0.0000 | Min. : 0 | Min. :0.0000 | Min. : 5.90 | Min. : 0.534 | |
| Class :character | Class :character | 1st Qu.:1982 | 1st Qu.: 1.020 | 1st Qu.: 33.732 | 1st Qu.: 308785 | 1st Qu.: 12.50 | 1st Qu.:27.904 | 1st Qu.: 9.475 | 1st Qu.: 46.245 | 1st Qu.: 0.1967 | 1st Qu.: 7020 | 1st Qu.: 175995379 | 1st Qu.: 473000000 | 1st Qu.: 2155665300 | 1st Qu.:60.375 | 1st Qu.:51.602 | 1st Qu.:42.43 | 1st Qu.: 296306400 | 1st Qu.: 183033447 | 1st Qu.: 66416 | 1st Qu.: -0.4758 | 1st Qu.:25.78 | 1st Qu.: 233742 | 1st Qu.: 3.567 | 1st Qu.: 0.05 | 1st Qu.: 18.038 | 1st Qu.: 94167420 | 1st Qu.: 25349827 | 1st Qu.:6.000 | 1st Qu.: -109276346 | 1st Qu.: 0 | 1st Qu.: 724234 | 1st Qu.: 881327 | 1st Qu.:48.91 | 1st Qu.:49.65 | 1st Qu.: 835377 | 1st Qu.: 1078178 | 1st Qu.: 21.503 | 1st Qu.: 622567 | 1st Qu.: 0.5894 | 1st Qu.: 22.669 | 1st Qu.: 3.2186 | 1st Qu.:53.36 | 1st Qu.:23.35 | 1st Qu.: 655 | 1st Qu.: 499.6 | 1st Qu.: 2.617 | 1st Qu.: -1023369537 | 1st Qu.: -11588042500 | 1st Qu.: -1303373808 | 1st Qu.: 28555001 | 1st Qu.: 2524760134 | 1st Qu.: 0.0000 | 1st Qu.: 12.70 | 1st Qu.: 1650 | 1st Qu.: 140 | 1st Qu.: 56.0697 | 1st Qu.: 7.842 | 1st Qu.:59.16 | 1st Qu.: 13450 | 1st Qu.: 1022938 | 1st Qu.: 2.296 | 1st Qu.: 0.137 | 1st Qu.: 1228323588 | 1st Qu.: 25.09 | 1st Qu.: 14.76 | 1st Qu.: 97.69 | 1st Qu.: 4028319213 | 1st Qu.: 695514598 | 1st Qu.: 10.63 | 1st Qu.: 355315735 | 1st Qu.: 544473558 | 1st Qu.: 928350408 | 1st Qu.: 747.9 | 1st Qu.: -0.4303 | 1st Qu.: 1.169 | 1st Qu.: 2187406538 | 1st Qu.: 0.462 | 1st Qu.: 7.109 | 1st Qu.: 6.289 | 1st Qu.: 8.460 | 1st Qu.: 895671076 | 1st Qu.: -0.4 | 1st Qu.: 0 | 1st Qu.: 0.000 | 1st Qu.: 29.70 | 1st Qu.: 0.000 | 1st Qu.: 1.699 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 26.01 | 1st Qu.: 0.000 | 1st Qu.: 0 | 1st Qu.:17.75 | 1st Qu.: 1.548 | 1st Qu.: 4.651 | 1st Qu.: 0.302 | 1st Qu.:12.28 | 1st Qu.: 737 | 1st Qu.: 45.616 | 1st Qu.: 0 | 1st Qu.: 0.0000 | 1st Qu.:21.75 | 1st Qu.: 0.4834 | 1st Qu.: 1157 | 1st Qu.:0.2379 | 1st Qu.:15.00 | 1st Qu.: 71.958 | |
| Mode :character | Mode :character | Median :1995 | Median : 2.323 | Median : 53.806 | Median : 2250864 | Median : 23.42 | Median :40.922 | Median : 15.700 | Median : 69.744 | Median : 1.8564 | Median : 30890 | Median : 973745600 | Median : 7987295600 | Median : 28709021350 | Median :77.149 | Median :64.447 | Median :51.69 | Median : 1228100000 | Median : 1112061730 | Median : 395478 | Median : 0.6670 | Median :46.19 | Median : 2090364 | Median :18.187 | Median : 13.67 | Median : 25.339 | Median : 589525169 | Median : 198990841 | Median :6.000 | Median : 0 | Median : 0 | Median : 4999443 | Median : 3041474 | Median :49.65 | Median :50.35 | Median : 3061896 | Median : 1999004 | Median : 31.689 | Median : 1300299 | Median : 1.5532 | Median : 71.453 | Median : 4.6771 | Median :60.53 | Median :34.40 | Median : 5266 | Median : 3290.0 | Median : 5.600 | Median : -119592786 | Median : -458840000 | Median : -172276599 | Median : 121305000 | Median : 49615015000 | Median : 0.0000 | Median : 31.20 | Median : 7480 | Median : 900 | Median : 72.8118 | Median :12.693 | Median :68.98 | Median : 100250 | Median : 3365515 | Median : 5.295 | Median : 6.828 | Median : 5788616733 | Median : 36.20 | Median : 20.63 | Median :102.96 | Median : 18780239280 | Median : 4452322244 | Median : 20.34 | Median : 3145461613 | Median : 3356117901 | Median : 4646974810 | Median : 2604.8 | Median : 1.9900 | Median : 3.608 | Median : 11109029840 | Median : 3.126 | Median :12.072 | Median : 16.298 | Median :12.711 | Median : 4830010764 | Median : 4.8 | Median : 1000000 | Median : 0.010 | Median : 65.34 | Median : 0.088 | Median : 18.028 | Median : 2.343 | Median : 0.000 | Median : 67.32 | Median : 1.599 | Median : 55 | Median :26.70 | Median : 2.306 | Median : 9.244 | Median : 2.345 | Median :18.14 | Median : 3689 | Median : 72.856 | Median : 0 | Median : 0.2701 | Median :34.29 | Median : 2.0131 | Median : 8140 | Median :0.3707 | Median :24.12 | Median : 99.655 | |
| NA | NA | Mean :1995 | Mean : 2.648 | Mean : 54.198 | Mean : 26466700 | Mean : 26.88 | Mean :41.536 | Mean : 23.048 | Mean : 81.157 | Mean : 6.5016 | Mean : 387149 | Mean : 14914706105 | Mean : 2785468080390 | Mean : 2980869017620 | Mean :71.795 | Mean :62.219 | Mean :51.33 | Mean : 29028656406 | Mean : 30179405859 | Mean : 5481252 | Mean : 0.4571 | Mean :45.80 | Mean : 30201302 | Mean :30.318 | Mean : 29.93 | Mean : 28.076 | Mean : 24343484422 | Mean : 23535540188 | Mean :6.155 | Mean : -1824322116 | Mean : 5287480742 | Mean : 56402276 | Mean : 31180725 | Mean :49.91 | Mean :50.09 | Mean : 30761528 | Mean : 9328817 | Mean : 34.495 | Mean : 2965607 | Mean : 1.6761 | Mean : 365.311 | Mean : 6.8307 | Mean :60.01 | Mean :33.16 | Mean : 92583 | Mean : 28317.0 | Mean : 8.913 | Mean : -409419037 | Mean : -908634940446 | Mean : -467566683 | Mean : 896634899 | Mean : 42138130034400 | Mean : 0.2629 | Mean : 45.12 | Mean : 73386 | Mean : 24281 | Mean : 67.8761 | Mean :13.249 | Mean :66.14 | Mean : 1294572 | Mean : 33196857 | Mean : 27.050 | Mean : 23.086 | Mean : 122229047620 | Mean : 43.31 | Mean : 20.92 | Mean :104.52 | Mean : 496913394315 | Mean : 72840190753 | Mean : 18.97 | Mean : 125588763362 | Mean : 106020810582 | Mean : 105299612634 | Mean : 9998.5 | Mean : 1.7475 | Mean : 3.439 | Mean : 426487447392 | Mean : 16.013 | Mean :13.614 | Mean : 27.288 | Mean :14.166 | Mean : 124664660105 | Mean : 145.8 | Mean : 4688399069 | Mean : 2.306 | Mean : 59.27 | Mean :16.145 | Mean : 32.431 | Mean : 17.886 | Mean : 4.937 | Mean : 75.38 | Mean : 14.240 | Mean : 106203 | Mean :29.86 | Mean : 2.329 | Mean :10.819 | Mean : 4.787 | Mean :19.83 | Mean : 99512 | Mean : 68.350 | Mean : 47892 | Mean : 11.5945 | Mean :34.68 | Mean : 4.7991 | Mean : 273387 | Mean :0.5263 | Mean :26.47 | Mean : 81.593 | |
| NA | NA | 3rd Qu.:2008 | 3rd Qu.: 3.972 | 3rd Qu.: 74.216 | 3rd Qu.: 8198393 | 3rd Qu.: 37.37 | 3rd Qu.:54.216 | 3rd Qu.: 26.487 | 3rd Qu.:100.452 | 3rd Qu.: 8.0866 | 3rd Qu.: 102256 | 3rd Qu.: 6139212749 | 3rd Qu.: 101718788500 | 3rd Qu.: 246596317750 | 3rd Qu.:84.619 | 3rd Qu.:73.996 | 3rd Qu.:59.91 | 3rd Qu.: 6856026932 | 3rd Qu.: 6663954605 | 3rd Qu.: 1363938 | 3rd Qu.: 1.8501 | 3rd Qu.:66.27 | 3rd Qu.: 8248653 | 3rd Qu.:52.394 | 3rd Qu.: 56.99 | 3rd Qu.: 35.381 | 3rd Qu.: 4354306253 | 3rd Qu.: 2024107348 | 3rd Qu.:7.000 | 3rd Qu.: 28504340 | 3rd Qu.: 53856043 | 3rd Qu.: 16543472 | 3rd Qu.: 9844537 | 3rd Qu.:50.35 | 3rd Qu.:51.09 | 3rd Qu.: 9836732 | 3rd Qu.: 6761455 | 3rd Qu.: 43.737 | 3rd Qu.: 3030611 | 3rd Qu.: 2.6003 | 3rd Qu.: 169.919 | 3rd Qu.: 9.9226 | 3rd Qu.:66.06 | 3rd Qu.:43.29 | 3rd Qu.: 38411 | 3rd Qu.: 10980.0 | 3rd Qu.: 9.968 | 3rd Qu.: -25619 | 3rd Qu.: 0 | 3rd Qu.: -4510681 | 3rd Qu.: 394237495 | 3rd Qu.: 639905504977 | 3rd Qu.: 0.0717 | 3rd Qu.: 67.38 | 3rd Qu.: 26628 | 3rd Qu.: 5321 | 3rd Qu.: 84.5323 | 3rd Qu.:17.586 | 3rd Qu.:73.98 | 3rd Qu.: 472710 | 3rd Qu.: 10295119 | 3rd Qu.: 11.047 | 3rd Qu.: 41.435 | 3rd Qu.: 31047568760 | 3rd Qu.: 54.60 | 3rd Qu.: 26.72 | 3rd Qu.:110.22 | 3rd Qu.: 109114709491 | 3rd Qu.: 32636282215 | 3rd Qu.: 27.57 | 3rd Qu.: 28533420208 | 3rd Qu.: 25108000000 | 3rd Qu.: 25565975639 | 3rd Qu.: 10402.0 | 3rd Qu.: 4.3246 | 3rd Qu.: 6.009 | 3rd Qu.: 72060508273 | 3rd Qu.: 14.523 | 3rd Qu.:18.441 | 3rd Qu.: 43.011 | 3rd Qu.:18.043 | 3rd Qu.: 32174092994 | 3rd Qu.: 10.7 | 3rd Qu.: 452500000 | 3rd Qu.: 1.528 | 3rd Qu.: 92.00 | 3rd Qu.:26.107 | 3rd Qu.: 60.488 | 3rd Qu.: 24.422 | 3rd Qu.: 0.000 | 3rd Qu.: 100.00 | 3rd Qu.: 21.823 | 3rd Qu.: 6300 | 3rd Qu.:37.93 | 3rd Qu.: 2.861 | 3rd Qu.:15.143 | 3rd Qu.: 5.106 | 3rd Qu.:25.67 | 3rd Qu.: 27396 | 3rd Qu.: 94.731 | 3rd Qu.: 7811 | 3rd Qu.: 16.8106 | 3rd Qu.:46.99 | 3rd Qu.: 6.3299 | 3rd Qu.: 56975 | 3rd Qu.:0.6085 | 3rd Qu.:37.39 | 3rd Qu.:100.000 | |
| NA | NA | Max. :2020 | Max. : 48.936 | Max. :100.000 | Max. :4352232429 | Max. : 100.00 | Max. :98.467 | Max. :304.276 | Max. :860.800 | Max. :87.5074 | Max. :45873850 | Max. :774147989000 | Max. : 651107600000000 | Max. :450281900000000 | Max. :96.093 | Max. :92.978 | Max. :96.20 | Max. :5884869109190 | Max. :6246125540220 | Max. :601000000 | Max. : 29.6283 | Max. :97.16 | Max. :3398794081 | Max. :98.343 | Max. :100.00 | Max. :100.236 | Max. :4858648262610 | Max. :4790066687040 | Max. :8.000 | Max. : 282689352952 | Max. :1257803920570 | Max. :7752840547 | Max. :3907216408 | Max. :76.71 | Max. :55.63 | Max. :3842820324 | Max. :409712858 | Max. :100.000 | Max. :37468302 | Max. : 17.6334 | Max. :21388.600 | Max. :28.3973 | Max. :86.40 | Max. :51.57 | Max. :12493789 | Max. :2986520.0 | Max. :192.227 | Max. :292301000000 | Max. : 105131000000000 | Max. : 257794000000 | Max. :167800328125 | Max. :10211700000000000 | Max. :22.4135 | Max. :219.30 | Max. :8174420 | Max. :3187680 | Max. :100.0000 | Max. :50.037 | Max. :85.42 | Max. :129956634 | Max. :3467973718 | Max. :23773.132 | Max. :100.000 | Max. :24723587089500 | Max. :427.58 | Max. : 100.67 | Max. :261.43 | Max. :87149338258100 | Max. :6256953481220 | Max. : 88.39 | Max. :23478971753200 | Max. :19262553026400 | Max. :19006596990600 | Max. :190512.7 | Max. :140.3670 | Max. :149.973 | Max. :87607773878100 | Max. :359.256 | Max. :94.057 | Max. :354.553 | Max. :62.416 | Max. :25247985795000 | Max. :844788.2 | Max. :1644540000000 | Max. :65.444 | Max. :100.00 | Max. :99.795 | Max. :100.000 | Max. :100.000 | Max. :87.986 | Max. :20422.89 | Max. :216.648 | Max. :15291329 | Max. :96.97 | Max. :103.158 | Max. :48.431 | Max. :86.957 | Max. :81.25 | Max. :10482498 | Max. :258.524 | Max. :7056781 | Max. :207.3675 | Max. :90.38 | Max. :360.8532 | Max. :34041046 | Max. :5.3510 | Max. :56.95 | Max. :100.000 | |
| NA | NA | NA | NA’s :85 | NA’s :60 | NA’s :83 | NA’s :3971 | NA’s :3819 | NA’s :3969 | NA’s :2895 | NA’s :1978 | NA’s :1542 | NA’s :3786 | NA’s :3742 | NA’s :4515 | NA’s :1101 | NA’s :1101 | NA’s :3731 | NA’s :3728 | NA’s :3726 | NA’s :3845 | NA’s :462 | NA’s :60 | NA’s :83 | NA’s :4604 | NA’s :5021 | NA’s :4571 | NA’s :3746 | NA’s :3752 | NA’s :527 | NA’s :4102 | NA’s :4541 | NA’s :32 | NA’s :950 | NA’s :927 | NA’s :927 | NA’s :950 | NA’s :4233 | NA’s :2726 | NA’s :2754 | NA’s :35 | NA’s :139 | NA’s :927 | NA’s :927 | NA’s :927 | NA’s :1948 | NA’s :1340 | NA’s :3186 | NA’s :2661 | NA’s :2622 | NA’s :3771 | NA’s :3713 | NA’s :3289 | NA’s :2618 | NA’s :1677 | NA’s :1360 | NA’s :1098 | NA’s :2198 | NA’s :3768 | NA’s :1015 | NA’s :116 | NA’s :4798 | NA’s :3295 | NA’s :4639 | NA’s :2874 | NA’s :2894 | NA’s :4765 | NA’s :3374 | NA’s :3309 | NA’s :4782 | NA’s :3230 | NA’s :3264 | NA’s :3730 | NA’s :3727 | NA’s :1835 | NA’s :2077 | NA’s :2074 | NA’s :1832 | NA’s :4516 | NA’s :4134 | NA’s :4154 | NA’s :4126 | NA’s :2875 | NA’s :4224 | NA’s :4668 | NA’s :4671 | NA’s :4671 | NA’s :4671 | NA’s :4671 | NA’s :4671 | NA’s :4772 | NA’s :3236 | NA’s :2433 | NA’s :2062 | NA’s :4775 | NA’s :4795 | NA’s :4775 | NA’s :4775 | NA’s :4775 | NA’s :2062 | NA’s :2433 | NA’s :2062 | NA’s :2433 | NA’s :4775 | NA’s :1963 | NA’s :1960 | NA’s :3046 | NA’s :808 | NA’s :5063 |
GoldPrices summary
knitr::kable(summary(goldPricesYearly))
| year | gold_usd | gold_gbp | gold_euro | |
|---|---|---|---|---|
| Length:54 | Min. : 35.96 | Min. : 15.01 | Min. : 261.6 | |
| Class :character | 1st Qu.: 283.00 | 1st Qu.: 179.26 | 1st Qu.: 344.1 | |
| Mode :character | Median : 382.50 | Median : 241.59 | Median : 926.3 | |
| NA | Mean : 580.96 | Mean : 374.95 | Mean : 805.1 | |
| NA | 3rd Qu.: 828.39 | 3rd Qu.: 441.39 | 3rd Qu.:1121.5 | |
| NA | Max. :1801.00 | Max. :1379.37 | Max. :1549.5 | |
| NA | NA | NA | NA’s :31 |
SpComposite summary
knitr::kable(summary(spCompositeYearly))
| year | spComposite | dividend | earnings | cpi | longInterestRate | realPrice | realDividend | realEarnings | cyclicallyAdjustedPERattio | |
|---|---|---|---|---|---|---|---|---|---|---|
| Length:151 | Min. : 3.136 | Min. : 0.1800 | Min. : 0.2058 | Min. : 6.462 | Min. : 0.8942 | Min. : 84.79 | Min. : 5.728 | Min. : 7.871 | Min. : 5.311 | |
| Class :character | 1st Qu.: 7.894 | 1st Qu.: 0.4217 | 1st Qu.: 0.5704 | 1st Qu.: 10.212 | 1st Qu.: 3.2185 | 1st Qu.: 186.28 | 1st Qu.: 9.365 | 1st Qu.: 14.529 | 1st Qu.:12.003 | |
| Mode :character | Median : 17.081 | Median : 0.8933 | Median : 1.4217 | Median : 20.042 | Median : 3.8196 | Median : 281.08 | Median :14.331 | Median : 22.892 | Median :16.457 | |
| NA | Mean : 332.147 | Mean : 6.9012 | Mean : 15.7599 | Mean : 62.619 | Mean : 4.5006 | Mean : 625.91 | Mean :17.637 | Mean : 35.241 | Mean :17.237 | |
| NA | 3rd Qu.: 160.446 | 3rd Qu.: 7.1400 | 3rd Qu.: 14.9679 | 3rd Qu.:101.742 | 3rd Qu.: 5.0492 | 3rd Qu.: 711.64 | 3rd Qu.:22.489 | 3rd Qu.: 43.987 | 3rd Qu.:20.774 | |
| NA | Max. :4114.705 | Max. :59.0941 | Max. :134.9175 | Max. :267.817 | Max. :13.9108 | Max. :4172.50 | Max. :62.339 | Max. :144.072 | Max. :42.068 | |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :10 |
CurrencyExchangerates summary
knitr::kable(summary(currencyExchangeRates))
| Date | Algerian.Dinar | Australian.Dollar | Bahrain.Dinar | Bolivar.Fuerte | Botswana.Pula | Brazilian.Real | Brunei.Dollar | Canadian.Dollar | Chilean.Peso | Chinese.Yuan | Colombian.Peso | Czech.Koruna | Danish.Krone | Euro | Hungarian.Forint | Icelandic.Krona | Indian.Rupee | Indonesian.Rupiah | Iranian.Rial | Israeli.New.Sheqel | Japanese.Yen | Kazakhstani.Tenge | Korean.Won | Kuwaiti.Dinar | Libyan.Dinar | Malaysian.Ringgit | Mauritian.Rupee | Mexican.Peso | Nepalese.Rupee | New.Zealand.Dollar | Norwegian.Krone | Nuevo.Sol | Pakistani.Rupee | Peso.Uruguayo | Philippine.Peso | Polish.Zloty | Qatar.Riyal | Rial.Omani | Russian.Ruble | Saudi.Arabian.Riyal | Singapore.Dollar | South.African.Rand | Sri.Lanka.Rupee | Swedish.Krona | Swiss.Franc | Thai.Baht | Trinidad.And.Tobago.Dollar | Tunisian.Dinar | U.A.E..Dirham | U.K..Pound.Sterling | U.S..Dollar | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Length:5978 | Min. : 71.29 | Min. :0.4833 | Min. :0.376 | Min. : 2.14 | Min. :0.0855 | Min. :0.832 | Min. :1.000 | Min. :0.917 | Min. :377.5 | Min. :6.093 | Min. : 833.2 | Min. :14.45 | Min. :4.665 | Min. :0.8252 | Min. :144.1 | Min. : 54.72 | Min. :31.37 | Min. : 2201 | Min. : 1699 | Min. :3.230 | Min. : 75.86 | Min. :117.2 | Min. : 756 | Min. :0.2646 | Min. :0.525 | Min. :2.436 | Min. :25.15 | Min. : 5.915 | Min. : 49.88 | Min. :0.3927 | Min. :4.959 | Min. :2.539 | Min. : 30.88 | Min. : 9.32 | Min. :24.55 | Min. :2.022 | Min. :3.64 | Min. :0.3845 | Min. :23.13 | Min. :3.745 | Min. :1.201 | Min. : 3.530 | Min. : 49.57 | Min. : 5.843 | Min. :0.7253 | Min. :24.44 | Min. :5.839 | Min. :1.342 | Min. :3.671 | Min. :1.213 | Min. :1 | |
| Class :character | 1st Qu.: 77.50 | 1st Qu.:0.6654 | 1st Qu.:0.376 | 1st Qu.: 2.59 | 1st Qu.:0.1197 | 1st Qu.:1.709 | 1st Qu.:1.348 | 1st Qu.:1.086 | 1st Qu.:503.5 | 1st Qu.:6.495 | 1st Qu.:1786.0 | 1st Qu.:19.35 | 1st Qu.:5.612 | 1st Qu.:1.0889 | 1st Qu.:202.7 | 1st Qu.: 70.28 | 1st Qu.:42.82 | 1st Qu.: 8855 | 1st Qu.: 1755 | 1st Qu.:3.676 | 1st Qu.:100.70 | 1st Qu.:145.4 | 1st Qu.:1013 | 1st Qu.:0.2854 | 1st Qu.:0.662 | 1st Qu.:3.188 | 1st Qu.:29.12 | 1st Qu.:10.953 | 1st Qu.: 68.33 | 1st Qu.:0.5813 | 1st Qu.:6.104 | 1st Qu.:2.755 | 1st Qu.: 51.79 | 1st Qu.:20.07 | 1st Qu.:43.18 | 1st Qu.:3.033 | 1st Qu.:3.64 | 1st Qu.:0.3845 | 1st Qu.:28.27 | 1st Qu.:3.745 | 1st Qu.:1.361 | 1st Qu.: 6.213 | 1st Qu.: 77.54 | 1st Qu.: 6.838 | 1st Qu.:0.9777 | 1st Qu.:31.50 | 1st Qu.:6.260 | 1st Qu.:1.566 | 1st Qu.:3.672 | 1st Qu.:1.519 | 1st Qu.:1 | |
| Mode :character | Median : 81.28 | Median :0.7595 | Median :0.376 | Median : 6.28 | Median :0.1528 | Median :2.048 | Median :1.468 | Median :1.297 | Median :538.6 | Median :6.989 | Median :2017.6 | Median :21.88 | Median :6.051 | Median :1.2295 | Median :224.3 | Median : 83.48 | Median :45.92 | Median : 9260 | Median : 8992 | Median :3.882 | Median :109.39 | Median :150.3 | Median :1122 | Median :0.2947 | Median :1.932 | Median :3.676 | Median :30.67 | Median :12.680 | Median : 74.04 | Median :0.6844 | Median :6.709 | Median :2.819 | Median : 60.75 | Median :22.94 | Median :44.40 | Median :3.290 | Median :3.64 | Median :0.3845 | Median :30.54 | Median :3.750 | Median :1.444 | Median : 7.480 | Median :103.99 | Median : 7.618 | Median :1.1878 | Median :34.65 | Median :6.282 | Median :1.723 | Median :3.672 | Median :1.599 | Median :1 | |
| NA | Mean : 90.59 | Mean :0.7683 | Mean :0.376 | Mean : 835.09 | Mean :0.1965 | Mean :2.161 | Mean :1.508 | Mean :1.268 | Mean :561.8 | Mean :7.316 | Mean :2073.1 | Mean :22.95 | Mean :6.281 | Mean :1.2076 | Mean :231.1 | Mean : 92.46 | Mean :48.02 | Mean : 9144 | Mean :10718 | Mean :4.003 | Mean :107.97 | Mean :185.6 | Mean :1100 | Mean :0.2936 | Mean :1.510 | Mean :3.508 | Mean :31.03 | Mean :13.116 | Mean : 77.37 | Mean :0.6606 | Mean :6.965 | Mean :2.960 | Mean : 70.24 | Mean :24.11 | Mean :45.01 | Mean :3.365 | Mean :3.64 | Mean :0.3845 | Mean :36.91 | Mean :3.749 | Mean :1.503 | Mean : 8.113 | Mean :102.19 | Mean : 7.741 | Mean :1.2090 | Mean :35.14 | Mean :6.310 | Mean :1.850 | Mean :3.672 | Mean :1.615 | Mean :1 | |
| NA | 3rd Qu.:108.88 | 3rd Qu.:0.8689 | 3rd Qu.:0.376 | 3rd Qu.: 6.28 | 3rd Qu.:0.1844 | 3rd Qu.:2.794 | 3rd Qu.:1.698 | 3rd Qu.:1.409 | 3rd Qu.:619.8 | 3rd Qu.:8.277 | 3rd Qu.:2482.9 | 3rd Qu.:24.94 | 3rd Qu.:6.805 | 3rd Qu.:1.3338 | 3rd Qu.:267.6 | 3rd Qu.:117.15 | 3rd Qu.:52.33 | 3rd Qu.:11380 | 3rd Qu.:11180 | 3rd Qu.:4.370 | 3rd Qu.:118.38 | 3rd Qu.:185.7 | 3rd Qu.:1186 | 3rd Qu.:0.3027 | 3rd Qu.:1.932 | 3rd Qu.:3.800 | 3rd Qu.:32.89 | 3rd Qu.:13.668 | 3rd Qu.: 86.80 | 3rd Qu.:0.7364 | 3rd Qu.:7.806 | 3rd Qu.:3.243 | 3rd Qu.: 94.29 | 3rd Qu.:28.44 | 3rd Qu.:47.10 | 3rd Qu.:3.822 | 3rd Qu.:3.64 | 3rd Qu.:0.3845 | 3rd Qu.:36.20 | 3rd Qu.:3.750 | 3rd Qu.:1.687 | 3rd Qu.: 9.995 | 3rd Qu.:126.29 | 3rd Qu.: 8.384 | 3rd Qu.:1.3903 | 3rd Qu.:39.45 | 3rd Qu.:6.382 | 3rd Qu.:2.157 | 3rd Qu.:3.672 | 3rd Qu.:1.676 | 3rd Qu.:1 | |
| NA | Max. :115.58 | Max. :1.1055 | Max. :0.376 | Max. :68827.50 | Max. :4.8414 | Max. :4.195 | Max. :1.851 | Max. :1.613 | Max. :758.2 | Max. :8.746 | Max. :3434.9 | Max. :40.29 | Max. :9.006 | Max. :1.5990 | Max. :318.7 | Max. :147.98 | Max. :68.78 | Max. :14850 | Max. :42000 | Max. :4.994 | Max. :147.00 | Max. :383.9 | Max. :1965 | Max. :0.3089 | Max. :1.932 | Max. :4.725 | Max. :36.50 | Max. :21.908 | Max. :109.98 | Max. :0.8822 | Max. :9.606 | Max. :3.522 | Max. :115.70 | Max. :32.53 | Max. :52.35 | Max. :4.500 | Max. :3.64 | Max. :0.3845 | Max. :83.59 | Max. :3.750 | Max. :1.851 | Max. :16.771 | Max. :157.65 | Max. :10.995 | Max. :1.8228 | Max. :56.06 | Max. :6.789 | Max. :2.509 | Max. :3.675 | Max. :2.102 | Max. :1 | |
| NA | NA’s :4112 | NA’s :263 | NA’s :69 | NA’s :3664 | NA’s :1275 | NA’s :539 | NA’s :1246 | NA’s :356 | NA’s :1220 | NA’s :1316 | NA’s :582 | NA’s :1850 | NA’s :251 | NA’s :1070 | NA’s :1415 | NA’s :354 | NA’s :429 | NA’s :1492 | NA’s :1312 | NA’s :1939 | NA’s :316 | NA’s :3051 | NA’s :601 | NA’s :1054 | NA’s :123 | NA’s :301 | NA’s :2460 | NA’s :2266 | NA’s :479 | NA’s :310 | NA’s :291 | NA’s :4297 | NA’s :488 | NA’s :4287 | NA’s :4198 | NA’s :1765 | NA’s :47 | NA’s :56 | NA’s :2435 | NA’s :46 | NA’s :259 | NA’s :535 | NA’s :509 | NA’s :349 | NA’s :239 | NA’s :565 | NA’s :657 | NA’s :4258 | NA’s :71 | NA’s :122 | NA |
worldIndicatorsForWholeWorld <- worldIndicatorsComplementedDf %>%
filter(Country_Name == 'World')
goldPricesAndWorldIndicators <- merge(x=worldIndicatorsForWholeWorld[-c(1,2)], y=goldPricesYearly[c('year','gold_usd')], by='year')
goldPricesAndSpcomposite = merge(x=goldPricesYearly[c('year','gold_usd')], y=spCompositeYearly, by='year')
control <- trainControl(method="repeatedcv", number=10, repeats=5)
rfGrid <- expand.grid(mtry = 10:30)
model <- train(
gold_usd~.,
data=goldPricesAndWorldIndicators,
method="rf",
na.action = na.pass,
tuneGrid = rfGrid,
ntree = 30,
trControl=control)
importance <- arrange(varImp(model)$importance, desc(Overall))
mostImoortantIndicators_Gold <- head(importance, n=15)
knitr::kable(mostImoortantIndicators_Gold)
| Overall | |
|---|---|
Birth_rate,_crude_(per_1,000_people) |
100.00000 |
Population_in_the_largest_city_(%_of_urban_population) |
43.47550 |
Rural_population_growth_(annual_%) |
41.39656 |
Population_ages_15-64_(%_of_total_population) |
34.09827 |
Population_ages_65_and_above_(%_of_total_population) |
31.13673 |
Population_density_(people_per_sq._km_of_land_area) |
28.76436 |
Secondary_education,_pupils |
21.43923 |
Labor_force,_total |
20.95800 |
Pupil-teacher_ratio,_primary |
19.75115 |
Population,_male_(%_of_total_population) |
19.60139 |
Service_imports_(BoP,_current_US$) |
18.48421 |
Services,_value_added_(%_of_GDP) |
16.83163 |
Gross_national_expenditure_(current_US$) |
16.67672 |
Population,_female |
16.54602 |
| Urban_population | 16.21152 |
names <- c(rownames(mostImoortantIndicators_Gold), 'year')
names <- gsub("`", "", all_of(names))
res <- cor(merge(worldIndicatorsComplementedDf[names], goldPricesAndSpcomposite, by='year'))
corrplot(res, method = 'square', order = 'AOE', type = 'lower', diag = FALSE, tl.cex=6.75, cl.cex=4)
g <- ggplot(
goldPricesYearly,
aes(x=year,
y=gold_usd,
group=1)
) +
scale_x_discrete(limits=goldPricesYearly$year,breaks=goldPricesYearly$year[seq(1,length(goldPricesYearly$year),by=5)]) +
geom_line()
ggplotly(g)
g <- ggplot(
goldPricesAndWorldIndicators,
aes(x=`CO2_emissions_from_residential_buildings_and_commercial_and_public_services_(%_of_total_fuel_combustion)`,
y=gold_usd,
group=1)
) +
geom_line()
ggplotly(g)
g <- ggplot(
goldPricesAndWorldIndicators,
aes(x=`Birth_rate,_crude_(per_1,000_people)`,
y=gold_usd,
group=1)
) +
geom_line()
ggplotly(g)
g <- ggplot(
goldPricesAndWorldIndicators,
aes(x=Rural_population,
y=gold_usd,
group=1)
) +
geom_line()
ggplotly(g)
g <- ggplot(
goldPricesAndWorldIndicators,
aes(x=`GDP_(current_US$)`,
y=gold_usd,
group=1)
) +
geom_line()
ggplotly(g)
g <- ggplot(
spCompositeYearly,
aes(x=year,
y=spComposite,
group=1)
) +
geom_line()
ggplotly(g)
cleanedWorldDevelopmentIndicatorsResultWithoutWorld <- cleanedWorldDevelopmentIndicatorsResult %>%
filter(Country_Name != 'World')
max_gdp_country = max(cleanedWorldDevelopmentIndicatorsResultWithoutWorld$`GDP_(current_US$)`, na.rm = TRUE)
scaleFactor <- max_gdp_country / max(cleanedWorldDevelopmentIndicatorsResultWithoutWorld$`GDP_per_capita_(current_US$)`, na.rm = TRUE)
g <- ggplot(
cleanedWorldDevelopmentIndicatorsResultWithoutWorld,
aes(x=year)
) +
geom_smooth(aes(y=`GDP_(current_US$)`), method="loess", col="blue") +
geom_smooth(aes(y=`GDP_per_capita_(current_US$)` * scaleFactor), method="loess", col="red") +
scale_y_continuous(name="GDP(current US$)", sec.axis=sec_axis(~./scaleFactor, name="GDP per capita(current US$)")) +
theme(
axis.title.y.left=element_text(color="blue"),
axis.text.y.left=element_text(color="blue"),
axis.title.y.right=element_text(color="red"),
axis.text.y.right=element_text(color="red")
) +
facet_wrap(vars(Country_Name), ncol = 4)
ggplotly(g)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1832 rows containing non-finite values (stat_smooth).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1835 rows containing non-finite values (stat_smooth).